Packet Video 99. A Corruption Model for Motion Compensated Video Subject to Bit Errors. Gustavo de los Reyes Amy R. Reibman Shih-Fu Chang

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1 A Corruptio Model for Motio Compesated Video Subject to Bit Errors Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag AT&T Labs AT&T Labs-Research Columbia Uiversity Abstract: We preset a model of how corruptio propagates i a block-based-ecoded video decoder whe the bitstream is subjected to bit errors. Our Markov model takes ito accout both corruptio itroduced via bit errors ad corruptio propagated via motio compesatio. The model icorporates the impact of spatial ad temporal source resiliece o limitig the propagatio of corruptio withi a frame ad across time. For a give video sequece, the model requires source-specific iputs characterizig motio ad spatial eergy. It also requires ecoder-specific iputs characterizig resiliece slice legth ad the proportio of I-blocks per frame. Oe fial parameter eeded by the model is the average umber of bits i a compressed block. We apply the model to the specific example of determiig the optimal allocatio of bit rate amog spatial resiliece, temporal resiliece, ad source rate i a trascoder that ijects resiliece ito the compressed bitstream.. Itroductio Desigig video codig systems for oisy chaels is a difficult problem that has bee receivig much attetio due to the icrease i the use of wireless devices. These efforts have resulted i recet video codig stadards for low-bitrate commuicatios that iclude methods for improvig spatial ad temporal source resiliece i additio to the usual chael codig methods [2]. Spatial ad temporal localizatio techiques limit the propagatio of errors withi a frame or to subsequet frames. Despite the umber of algorithms that have bee proposed for improvig the resiliece of video over oisy chael [4, 6], few aalytical models have bee proposed to support these ad other algorithms. I this paper, we derive a Markov model that describes how corruptio, iitially caused by bit errors durig trasport, propagates i videos that are coded usig motio compesatio. Our model takes ito accout the effects of spatial ad temporal resiliece as a meas for limitig the propagatio of the corruptio. Budagavi [] preseted a model for error propagatio usig a restrictive motio model where errors affect oly future blocks i the same spatial locatio as the origial error. I this paper, we allow urestricted block-based motio compesatio as defied i H.263 [2] ad similar block-based ecodig stadards. Hece the model is applicable to a wide rage of compressed video sequeces. I additio, little work has bee doe to combie the various methods of resiliece ad provide guidace as to how best to combie the methods give a fixed bitrate budget. I [3], we described a trascoder that ijects combied spatial ad temporal source resiliece ito the compressed bitstream at the wireless base statio or mobile switch. I this paper, we use our Markov model to derive operatioal rate distortio fuctios for spatial ad temporal source resiliece, ad use these to determie the optimal bitrate allocatio amog source rate, spatial resiliece, ad temporal resiliece. We use software simulatio to verify the results obtaied from our model. 2. Resiliece I our model, we focus o techiques for spatial ad temporal localizatio. These methods prevet sigal errors caused by a bit error or packet loss from propagatig withi a video frame or to subsequet frames. Spatial error propagatio ca occur whe the decoder loses sychroizatio while decodig the variable legth codes. I this paper, we limit spatial error propagatio by isertig additioal sychroizatio headers to reduce the umber of blocks i a slice. With a shorter slice, the decoder re-sychroizes more quickly, resultig i less lost data. For temporal localizatio, we trasmit more itra-frames (I-frames) or I-blocks. These frames or blocks are coded to be temporally idepedet Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. of

2 of previous frames or blocks ad are used as refereces for subsequet temporal predictio. More frequet I-blocks reduce the duratio of error propagatio. The temporal ad spatial localizatio techiques improve resiliece by icreasig the overall bitrate. Therefore, we also use rate-reductio techiques to recover some bit-rate to allocate for improvig resiliece. Rate reductio techiques fall ito two geeral classes: requatizig coefficiets ad discardig coefficiets. I this paper, we take the simple approach of discardig coefficiets to reduce the rate i a give frame by usig a zoal mask, where the size of the mask is selected to provide the required rate reductio. It is the cotetio for bit rate betwee resiliece ad source rate that leads us to use rate distortio theory as a guide for selectig optimal bit rates as discussed below. 3. Overview of Corruptio Propagatio Model I this sectio we describe the motivatio for usig a Markov model, ad we describe the basic compoets of the model. Blocks withi a frame ca be corrupted for two mai reasos: bit errors occur durig trasport, or corrupted iformatio from the previous frame is used durig motio compesatio. Therefore, the umber of corrupted blocks i the curret frame oly depeds o the iput errors for the curret frame, the motio vectors i the curret frame, ad the umber of corrupted blocks i the previous frame. This fits aturally ito the structure of a Markov model. The overall corruptio propagatio model calculates the umber of corrupted blocks i each video frame which is the sum of the umber of blocks corrupted due to bit errors ad the umber of blocks corrupted due to motio compesatio mius the itersectio betwee the two types of corrupted blocks. The umber of corrupted blocks, X, withi frame,, is the: ( X X ) X X, mc + X, loss, mc, loss = (Eq. ) where mc idicates corruptio due to motio compesatio, ad loss idicates corruptio due to bit errors. For blocks that are lost durig trasport due to bit errors, X,loss, we derive a statistical model for the expected umber of blocks lost as a fuctio of the spatial resiliece (slice legth) ad the bit error rate (BER) i the chael. For blocks that are corrupted durig motio compesatio, X,mc, we derive a Markov model that icludes the probability that a block is corrupted by referecig a block that has previously bee corrupted. Withi each frame, the probability that a block is corrupted by motio compesatio is treated as a Beroulli process with probability of success, p. This probability depeds heavily o the motio i the video ad the corruptio i the previous frame. Sice the corruptio i the previous frame icludes blocks that were lost to bit errors, the Markov model icludes the effects of the spatial resiliece model. We will show that this Markov model also icludes the effects of temporal resiliece. To fold the spatial model ito the Markov model, we simplify Equatio ad use the expected value of X,loss,. Equatio the becomes. X = X, mc X where r = F + E [ X, loss ] ( X, mc E[ X, loss ]) X E[ X ] = re[ X ] We estimate the itersectio as,,, ad F mc is the total umber of loss loss blocksi a frame. (Eq. 2) I the ext sectios we describes the spatial model ad the Markov model i more detail. 4. Spatial Resiliece Model I this sectio we derive a statistical model of the video quality as a fuctio of the spatial resiliece (as measured by slice legth) ad the chael coditios (as measured by mea bit error rate Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 2 of

3 (BER)). This model takes ito accout the corruptio propagatio withi a frame caused by bit errors durig trasport. This spatial resiliece model is oe part of the overall error propagatio model. 4. Derivatio of Spatial Resiliece Model The importat parameters i this model are slice legth, BER, ad the umber of bits per block. The slice legth is measured i macroblocks as described i [2]. I this paper, we use the more geeric term blocks rather tha the specific term macroblocks. However, durig implemetatio, we use the specific defiitio of macroblocks i [2]. For this aalysis, we oly use mea BER as the idicator of wireless chael coditios ad assume bit errors are idepedet i time. We assume that oce the decoder detects a error, it discards the block cotaiig the error ad all subsequet blocks util the ext slice header. We further assume that every bit error results i a error that is detected by the decoder. This overestimates the umber of lost blocks but does ot characterize the distortio caused by errors that caot be detected from video sytax. For the spatial resiliece model, we use a probabilistic model for the expected block losses. The probability that block m ad all subsequet blocks i the slice are lost is equal to the probability that o bit errors occur i blocks to (m-) ad at least oe bit error occurs i block m. Therefore, if we assume a costat block legth i bits, the expected umber of blocks lost per slice is: [ ] ib+ c ( i+ ) b+ c {( legth i)( P ) ( P ) } X legth, loss E = e e slice i= where X, loss are the blocks lost, i is the block positio, legth is the legth of the slice i blocks, Pe is the probability of error (or BER), c is the header legth, ad b is the costat legth of the block size i bits. (Eq. 3) 4.2 Effect of Spatial Resiliece The result of the spatial resiliece model is show i Figure, which shows how addig more slice headers (reducig the slice legth) leads to a lower Mea Square Error (MSE) per video frame, especially at higher BER. This figure also shows strog o-liearities across both slice legth ad BER. Oe of our goals is to take advatage of these o-liearities to select a operatig poit for spatial resiliece that produces the best video quality i the presece of bit errors. 3 MSE (per video frame) Slice legth (blocks) Log(BER) -3-2 Figure. Effect of spatial resiliece ad wireless chael coditios o block loss. Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 3 of

4 5. Overall Corruptio Propagatio Model I this sectio we derive the Markov model that describes the propagatio of corruptio due to motio compesatio. Also, we iclude the results of the spatial model i the motio compesatio model ad derive a overall corruptio-propagatio model. 5. Corruptio due to Motio Compesatio We begi this derivatio by defiig a trasitio matrix that govers the propagatio of corruptio i frame + durig motio compesatio give a umber of corrupted blocks i frame. As discussed previously, our model treats the probability of corruptio due to motio compesatio as a Beroulli process with probability of success, p. The motio compesatio trasitio matrix is the defied by a biomial distributio: k j k j P( i, jmc ) = P{ X +, mc = jmc X = i} = p mcq mc j mc where X is the umber of corrupted blocks i frame, (Eq. 4) p is the probability a block is corrupted via motio compesatio, q = p, k is the umber of blocks i a frame, ad i, jmc =,,, k. Note that this equatio describes how motio compesatio propagates corruptio from the previous (referece) frame. This equatio does ot specify how the corruptio occurred i the previous frame. That will be described i the ext sectios. 5.2 Corruptio due to Motio Compesatio ad Bit Errors Recall that Equatio 2 describes how we model corruptio due joitly to bit errors, X,loss, ad motio compesatio, X,mc. By substitutig Equatio 2 ito Equatio 4 ad solvig for X +, we derive the trasitio matrix that govers the overall propagatio of errors from frame to frame. k j k j P( i, j) = P{ X + = j X = i} = p q j where X is the umber of corrupted blocks i frame, (Eq. 5) p is the probability a block is corrupted via motio compesatio, q = p, k is the umber of blocks i a frame, ad i, j =,,, k. Recall that P(i, j) is ideed a fuctio of i because p is a fuctio of the corruptio i the referece frame,. We will derive a equatio for p shortly. Figure 2 shows schematically how the spatial model ad the motio-compesated Markov model are used together to describe the overall corruptio propagatio. The large squares idicate video frames, ad the smaller squares idicate lost blocks withi the frames. The letters, M, ad L are used to idicate et corrupted blocks, corruptio due to motio compesatio, ad losses due to chael bit errors, respectively. I the iitial video frame, losses are itroduced due to bit errors. These losses are propagated via motio compesatio as described i Equatio 4. Accordig to Equatio 2, ew losses are itroduced i the ext frame due to bit errors, ad itersectig losses are removed. This results i the et corruptio due to both bit-error losses ad motio compesatio. However, by usig the trasitio matrix of Equatio 5, the et result from Frame to Frame ca be calculated directly. The the process repeats for the followig frame. Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 4 of

5 X X =E[X,loss ] X,mc X,mc + E[X,loss ] = X,mc + E[X,loss ] -X,mc E[X,loss ] M M ML ML M M M M L M L M M M Frame Frame E[X,loss ] Motio Compesatio P(i, j mc ) E[X,loss ] Motio Compesatio P(i, j mc ) P(i, j) Figure 2. Schematic propagatio of corruptio via bit errors ad motio compesatio. Now that we have the trasitio matrix, we ca determie the expected umber of corrupted blocks due to motio compesatio ad bit errors i each video frame,, from the -step trasitio probabilities: P { X = j X = i} ( i, j) = P (Eq. 6) The expected umber of corrupted blocks due to motio compesatio i each frame i the video is: E [ X X = i] = jp{ X = j X = i} = j=,,, k jp j=,,, k where i is ( i, j) the umber of corrupted blocks i the first frame,. (Eq. 7) From Equatio 7, oe ca see how the model describes the error propagatio frame by frame. The corruptio i each frame,, is just a fuctio of the -step trasitio probabilities for that frame. Now we determie the probability, p, that a block is corrupted through motio compesatio so that we ca compute the trasitio matrix, P(i, j) of Equatio 5. A block i the curret frame will be corrupted through motio compesatio if it refereces corrupted iformatio (pixels) i the previous frame. Sice we are workig with blocks as the uit of measure rather tha pixels, this is equivalet to sayig that a block i the curret frame will be corrupted if it refereces a corrupted block, ad it refereces corrupted pixels withi that corrupted block. To compute these probabilities, we group the blocks ito four categories characterized by their motio vectors (MVs). The first category is for blocks that have MVs that describe o motio. The secod category is for blocks that have MVs that describe horizotal or vertical motio. The third category is for blocks that have MVs that describe combied horizotal ad vertical motio. The last category is for I-blocks that have o associated MVs. For each category of block, we compute the probability, p i, that a block of that type is corrupted via motio compesatio. The overall probability of corruptio for a block give a certai amout of corruptio i the previous frame is the: p = p P( block ) (Eq. 8) i i=,2,3,4 i Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 5 of

6 where i is oe of the four possible categories i = is a block of o motio, i = 2 is a block of horizotal or vertical motio, i = 3 is a block of combied horizotal ad vertical motio, ad i = 4 is a I-block. For I-blocks, the probability of a I-block beig corrupted by motio compesatio is clearly zero. O the other had, for a block with o motio, the probability of corruptio is oe if it refereces a corrupted block. For these blocks, the probability of corruptio is simply the probability that a block i the refereced frame is corrupted, which we estimate by the proportio of blocks i the previous frame that are corrupted, r. Calculatig the probability of corruptio for the other two categories of blocks is somewhat more ivolved. These blocks ca referece either two or four blocks i the previous frame, ad various combiatios of corrupted ad ot corrupted blocks ca exist for these refereced blocks. Furthermore, to be corrupted, a block must still referece the corrupted iformatio i a corrupted block. For these two categories of blocks, the probability that a block refereces oe or more corrupt blocks, p ref, ca be described by a biomial distributio: l l j l j l pref = pcorr ( pcorr ) = ( pcorr ) j= j where pcorr is the probability a block i the referece frame is corrupted, ad l is the umber of blocks that a curret block refereces : l = for o motio, l = 2 for vertical or horizotal motio, ad l = 4 for combied vertical ad horizotal motio.. 9) As before, we estimate the probability that a block i the refereced frame is corrupted by the proportio of blocks i the refereced frame that are corrupted, p corr = r. Thus far, we have estimated the probability that a block i the curret frame refereces a corrupted block. Now we estimate the probability that a block i the curret frame refereces corrupted iformatio give that it refereces a corrupted block. For each block, we assume a uiform distributio of corruptio from miimum to complete corruptio. The, the fial equatio for the probability that a block is corrupted through motio compesatio, p, is ow: (Eq p = a rp( block ad ) + a 2 2 [ ( r) ] P( block ) + a ( r) 2 4 [ ] P( block P( blocki ) = =,2,3,4 (Eq. ) i 3 3 ) where block a ( = ), a 2 i are the categories of blocks defied above, ad, ad a blocks of category block, 3 are the probability of referecig corrupt iformatio i block, ad block 2 3, respectively. 5.3 Effect of Temporal Resiliece I this sectio we show how our model icorporates the effect of usig additioal I-blocks for temporal resiliece. I Equatio, sice the probability of all four categories of blocks must sum to oe, as the proportio of I-blocks, P(block 4 ), icreases, the probability of receivig the other blocks, P(block ) + P(block 2 ) + P(block 3 ), decreases. This decreases p, the probability that a block is corrupted via motio compesatio. This behavior is show i Figure 3, which shows the ivariat (steady state) Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 6 of

7 distributio of the Markov model with a BER = 5x -4, ad two differet probability of I blocks, P(block 4 ). O average, there are fewer corrupted blocks as P(block 4 ) icreases. P(X = j) [Ivariat (steady state) distributio] P(I-block)=.5, BER = 5x -4 P(I-block)=.25, BER = 5x j (Number of blocks corrupted) Figure 3. Ivariat distributio of Markov model as a fuctio of P(block 4 )=probability of I blocks. 6. Optimal Allocatio of Bit Rate I the rest of the paper we show oe way that we use the corruptio-propagatio models: to optimally allocate resiliece. Give that we ca allocate bit rate over three sources source rate, spatial resiliece, ad temporal resiliece we would like to allocate the bit rate optimally to miimize distortio i the video. Applyig rate distortio theory to our resiliece problem, we wat to fid a vector of rates which miimizes a overall distortio measure subject to a rate costrait, B. Solvig this costraied optimizatio problem is equivalet to solvig the ucostraied optimizatio problem with a objective fuctio, H: H = D( L) + λr( L) where L is the vector of resources, λ is the Lagragia multiplier, D is the distortio that results from applyig the resources, ad R is the rate that results from applyig the resources. (Eq. ) The objective fuctio ca be writte as: [ Di ( Li ) + λri ( Li )] H (Eq. 2) = N i= Solvig this set of simultaeous equatios leads to: D DN λ = = = R RN (Eq. 3) subject to R( L) B That is, give that there is at least oe solutio to the problem, the solutios exist at the poits where the slopes of the distortio-rate fuctios D i (R i ) are equal, ad the sum of the rates R i are less tha or equal to the available bit rate. I our case, the vector of resources icludes the spatial resiliece, the temporal Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 7 of

8 resiliece, ad the source rate. We have a additioal costrait i that the resources vary i discrete amouts. For example, slice legths vary i iteger values from oe to 22. Therefore, the bit rates used by the resources also vary i discrete amouts. This discreteess affects the optimal solutio. We cosider the optimal solutio to be the oe where the total bit rate comes closest to the available bit rate of the chael. Also, we defie the rate distortio fuctios as operatioal fuctios that are geerated from the Markov models or from simulatios. 7. Trascoder I [3] we exteded the use of trascodig beyod rate reductio [5], to iclude the additio of resiliece iformatio i the bitstream. The trascoder operatio is show i Figure 4. The trascoder decodes the icomig bitstream to the degree required to add resiliece. Resiliece is added by the trascoder, ad, if ecessary, the bit-rate is reduced. The the resiliet bitstream is re-quatized (for temporal resiliece oly) ad variable legth re-ecoded. Icomig ecoded bitstream Decode video, as required Add spatial ad/or temporal resiliece Reduce rate of bitstream Figure 4. High-level trascoder operatio. Resiliet bitstream at ear-origial rate. Re-quatize ad re-ecode, as required The spatial localizatio techique of addig more slice headers requires oly simple bitstream parsig ad arithmetic operatios. A variable-legth decoder parses the bitstream ad iserts additioal slice start codes where ecessary. I additio, motio vectors (MVs) are decoded, a ew differetial MV is calculated from referece MVs oly withi the curret slice, ad the result is variable legth ecoded. The trascoder requires more complicated processig to icrease the temporal resiliece by usig more frequet I-blocks. The icomig bitstream is fully decoded, icludig motio compesatio, to create the ew I-blocks. The coefficiets of the ew I-blocks are the DCT-coded, re-quatized, scaed, ad variable-legth ecoded. Because the trascoder ca modify the resiliece to match the prevailig chael coditios, a method to determie the optimal resiliece is ivaluable. 8. Results The operatioal rate distortio fuctio (ORDF) for source rate was geerated from simulatio, while the ORDF for spatial ad temporal resiliece were geerated from our aalytical models. I additio, the models estimate block losses ad corruptio that must be coverted to distortio to produce the ORDF. This coversio is a fuctio of the video cotet, so we estimated the distortio by simulatig losses i the decoded video. For each of the ORDF i the figures below, we curve fitted the data ad the differetiated the fit curves to get smooth slopes as a fuctios of bit rate. The figures below show the origial ORDF with the superimposed curve fit o oe plot, ad the correspodig slope fuctios o the adjacet plot. Figures 5-7 show the operatioal rate distortio fuctios ad their slopes at a BER of 5x -4 for source rate, spatial resiliece (slice legth), ad temporal resiliece (I-block proportio), respectively. The y-axis for the slope of the spatial resiliece ORDF (Figure 6) has bee scaled to highlight the area of iterest. For source rate, the x-axes are the ratio of the resultig bit rate whe coefficiets are dropped to the origial bit rate with all coefficiets. For spatial resiliece ad temporal resiliece, the x-axes are the ratio of the resultig bit rate whe resiliece is added to the baselie resiliece (mius ). Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 8 of

9 Distortio (MSE) Bit rate decrease due to Dropped Coefficets Slope d(distortio)/d(rate) Bit rate decrease due to Dropped Coefficiets Figure 5. Operatioal rate distortio fuctio ad slope fuctio for source rate (from simulatio). 4 Distortio (MSE) Slope d(distortio)/d(rate) Bit rate icrease due to Slices (relative to GOB) Bit rate icrease due to Slices Figure 6. Operatioal rate distortio fuctio ad slope fuctio for spatial resiliece (aalytical model). 8-5 Distortio (MSE) Slope d(distortio)/d(rate) Bit rate icrease due to I-blocks Bit rate icrease due to I-blocks Figure 7. Operatioal rate distortio fuctio ad slope fuctio for temporal resiliece (Markov model). The operatig poit is selected by tryig to match slopes such that the total bit rate is less tha or equal to the bit rate of the origial video. However, the possible solutios are further costraied because the resources vary i discrete amouts. I additio, the effect of MSE due to source rate is perceptually differet tha the effect of MSE due to resiliece. Decreasig the source rate teds to blur the video, while decreasig spatial ad temporal resiliece teds to icrease the umber of severe artifacts. For these severe artifacts, several blocks of the video may chage color, or parts of the video become mismatched. Therefore, a certai amout of judgemet must be applied whe selectig the optimal solutio. Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. 9 of

10 I [3] we used a trial-ad-error approach to select the operatig poit, while here we described a optimal method for selectig the operatig poit. The optimal operatig poit for a BER of 5x -4 correspods to a slice legth of 4 blocks, a I-block proportio equal to each block beig refreshed every 5 frames, ad a source rate such that a maximum of coefficiets are retaied i each block. This results i a Peak Sigal to Noise Ratio (PSNR) of 8. db. The results preseted i [3] for the same BER resulted i a PSNR of 7.4 db for a slice legth of 5 blocks, a I-block proportio equal to each block beig refreshed every frames, ad a source rate such that a maximum 5 coefficiets are retaied i each block. The result for the baselie video with omial resiliece is a PSNR of 3.5 db. For omial resiliece we use a slice legth of 22 blocks ad a sigle I-frame followed by all P-frames. We also computed the optimal resiliece for a BER of -4. The operatig poit with the optimal method is a slice legth of blocks, a I-block proportio equal to each block beig refreshed every 7 frames, ad a source rate such that a maximum 6 coefficiets are retaied i each block. This results i a Peak Sigal to Noise Ratio (PSNR) of 2.4 db. This is slightly less tha the PSNR of 2.8 db preseted i [3] for the same BER. However, both methods lead to much better results tha the baselie video with omial resiliece ad a PSNR of 8.5 db. Therefore, we ca achieve early the same results with the optimal approach as compared to our previous trial-ad-error approach. This is a great beefit sice the optimal approach depeds more o aalytical models ad less o the specific video sequece. This makes the optimal approach better suited to ear-real-time calculatios that ca be used i a resiliece trascoder. 9. Summary ad Coclusios We developed a aalytical model for the propagatio of corruptio as a fuctio of spatial ad temporal resiliece ad BER. For a give video sequece, the model requires source-specific iputs characterizig motio ad spatial eergy. The spatial eergy of the video is used to estimate the distortio (MSE) that results from block losses ad corruptio. For a system implemetatio, a likely place to estimate this distortio is at a scee chage. The eergy cotet per frame betwee scees chages will be reasoably steady, ad methods exist for determiig scee chages i real time. The model also takes as iput the ecoder-specific parameters of slice legth ad I-block proportio per frame. We apply the model to the specific example of determiig the optimal allocatio of bit rate amog spatial resiliece, temporal resiliece, ad source rate i a trascoder that ijects resiliece ito the compressed bitstream. The optimal approach leads to early the same results tha previously reported whe usig a trial-ad-error approach.. Refereces [] Budagavi, M. ad J. D. Gibso. Error Propagatio i Motio Compesated Video over Wireless Chaels, IEEE Iteratioal Coferece o Image Processig, October 997. [2] Draft ITU-T Recommedatio H.263, Video Codig for Low Bitrate Commuicatio, Sept [3] De los Reyes, G., A. R. Reibma, J. C. Chuag, ad S.-F. Chag, Video Trascodig for Resiliece i Wireless Chaels, IEEE Iteratioal Coferece o Image Processig, October 998. [4] Farber. N., E. Steiback, ad B. Girod, Robust H.263 Compatible Video Trasmissio over Wireless Chaels, Iteratioal Picture Codig Symposium, Melboure, Australia, March 996. [5] Keesma, G., R. Hellighuize, F. Hoeksema, G. Heidma, Trascodig of MPEG Bitstreams, Sigal Processig: Image Commuicatios, 8 (996), pp [6] Liao, J. Y. ad J. D. Villaseor, Adaptive Itra Update for Video Codig over Noisy Chaels. IEEE Iteratioal Coferece o Image Processig, September 996. Gustavo de los Reyes Amy R. Reibma Shih-Fu Chag 8/26/99 p. of